


import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    _lowercase			:				List[str] 						=						OrderedDict()
    for key, value in state_dict.items():
        if key.startswith('module.encoder'					):
            _lowercase			:				List[Any] 						=						key.replace('module.encoder'				,					'glpn.encoder'					)
        if key.startswith('module.decoder'					):
            _lowercase			:				Optional[int] 						=						key.replace('module.decoder'				,					'decoder.stages'					)
        if "patch_embed" in key:
            # replace for example patch_embed1 by patch_embeddings.0
            _lowercase			:				Tuple 						=						key[key.find('patch_embed'					) + len('patch_embed'					)]
            _lowercase			:				Union[str, Any] 						=						key.replace(F'''patch_embed{idx}'''				,					F'''patch_embeddings.{int(lowerCamelCase_					)-1}'''					)
        if "norm" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('norm'				,					'layer_norm'					)
        if "glpn.encoder.layer_norm" in key:
            # replace for example layer_norm1 by layer_norm.0
            _lowercase			:				Dict 						=						key[key.find('glpn.encoder.layer_norm'					) + len('glpn.encoder.layer_norm'					)]
            _lowercase			:				Dict 						=						key.replace(F'''layer_norm{idx}'''				,					F'''layer_norm.{int(lowerCamelCase_					)-1}'''					)
        if "layer_norm1" in key:
            _lowercase			:				Dict 						=						key.replace('layer_norm1'				,					'layer_norm_1'					)
        if "layer_norm2" in key:
            _lowercase			:				Dict 						=						key.replace('layer_norm2'				,					'layer_norm_2'					)
        if "block" in key:
            # replace for example block1 by block.0
            _lowercase			:				Optional[int] 						=						key[key.find('block'					) + len('block'					)]
            _lowercase			:				str 						=						key.replace(F'''block{idx}'''				,					F'''block.{int(lowerCamelCase_					)-1}'''					)
        if "attn.q" in key:
            _lowercase			:				List[Any] 						=						key.replace('attn.q'				,					'attention.self.query'					)
        if "attn.proj" in key:
            _lowercase			:				Tuple 						=						key.replace('attn.proj'				,					'attention.output.dense'					)
        if "attn" in key:
            _lowercase			:				Optional[int] 						=						key.replace('attn'				,					'attention.self'					)
        if "fc1" in key:
            _lowercase			:				Dict 						=						key.replace('fc1'				,					'dense1'					)
        if "fc2" in key:
            _lowercase			:				Optional[int] 						=						key.replace('fc2'				,					'dense2'					)
        if "linear_pred" in key:
            _lowercase			:				str 						=						key.replace('linear_pred'				,					'classifier'					)
        if "linear_fuse" in key:
            _lowercase			:				List[Any] 						=						key.replace('linear_fuse.conv'				,					'linear_fuse'					)
            _lowercase			:				str 						=						key.replace('linear_fuse.bn'				,					'batch_norm'					)
        if "linear_c" in key:
            # replace for example linear_c4 by linear_c.3
            _lowercase			:				int 						=						key[key.find('linear_c'					) + len('linear_c'					)]
            _lowercase			:				str 						=						key.replace(F'''linear_c{idx}'''				,					F'''linear_c.{int(lowerCamelCase_					)-1}'''					)
        if "bot_conv" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('bot_conv'				,					'0.convolution'					)
        if "skip_conv1" in key:
            _lowercase			:				int 						=						key.replace('skip_conv1'				,					'1.convolution'					)
        if "skip_conv2" in key:
            _lowercase			:				List[Any] 						=						key.replace('skip_conv2'				,					'2.convolution'					)
        if "fusion1" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('fusion1'				,					'1.fusion'					)
        if "fusion2" in key:
            _lowercase			:				Tuple 						=						key.replace('fusion2'				,					'2.fusion'					)
        if "fusion3" in key:
            _lowercase			:				Optional[Any] 						=						key.replace('fusion3'				,					'3.fusion'					)
        if "fusion" in key and "conv" in key:
            _lowercase			:				Tuple 						=						key.replace('conv'				,					'convolutional_layer'					)
        if key.startswith('module.last_layer_depth'					):
            _lowercase			:				Dict 						=						key.replace('module.last_layer_depth'				,					'head.head'					)
        _lowercase			:				str 						=						value

    return new_state_dict



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Optional[Any]:
    # for each of the encoder blocks:
    for i in range(config.num_encoder_blocks					):
        for j in range(config.depths[i]					):
            # read in weights + bias of keys and values (which is a single matrix in the original implementation)
            _lowercase			:				Tuple 						=						state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''					)
            _lowercase			:				str 						=						state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''					)
            # next, add keys and values (in that order) to the state dict
            _lowercase			:				int 						=						kv_weight[
                : config.hidden_sizes[i], :
            ]
            _lowercase			:				int 						=						kv_bias[: config.hidden_sizes[i]]
            _lowercase			:				str 						=						kv_weight[
                config.hidden_sizes[i] :, :
            ]
            _lowercase			:				Any 						=						kv_bias[config.hidden_sizes[i] :]



def 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				int 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    _lowercase			:				Optional[Any] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)

    return image



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False				,					lowerCamelCase_=None					)       ->		Optional[int]:
    _lowercase			:				List[Any] 						=						GLPNConfig(hidden_sizes=[64, 128, 320, 512]				,					decoder_hidden_size=64				,					depths=[3, 8, 27, 3]					)

    # load image processor (only resize + rescale)
    _lowercase			:				Optional[Any] 						=						GLPNImageProcessor()

    # prepare image
    _lowercase			:				List[Any] 						=						prepare_img()
    _lowercase			:				Union[str, Any] 						=						image_processor(images=lowerCamelCase_				,					return_tensors='pt'					).pixel_values

    logger.info('Converting model...'					)

    # load original state dict
    _lowercase			:				Tuple 						=						torch.load(lowerCamelCase_				,					map_location=torch.device('cpu'					)					)

    # rename keys
    _lowercase			:				Any 						=						rename_keys(lowerCamelCase_					)

    # key and value matrices need special treatment
    read_in_k_v(lowerCamelCase_				,					lowerCamelCase_					)

    # create HuggingFace model and load state dict
    _lowercase			:				Tuple 						=						GLPNForDepthEstimation(lowerCamelCase_					)
    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    # forward pass
    _lowercase			:				Optional[Any] 						=						model(lowerCamelCase_					)
    _lowercase			:				Union[str, Any] 						=						outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            _lowercase			:				List[str] 						=						torch.tensor(
                [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]					)
        elif "kitti" in model_name:
            _lowercase			:				List[str] 						=						torch.tensor(
                [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]					)
        else:
            raise ValueError(F'''Unknown model name: {model_name}'''					)

        _lowercase			:				List[Any] 						=						torch.Size([1, 480, 640]					)

        assert predicted_depth.shape == expected_shape
        assert torch.allclose(predicted_depth[0, :3, :3]				,					lowerCamelCase_				,					atol=1e-4					)
        print('Looks ok!'					)

    # finally, push to hub if required
    if push_to_hub:
        logger.info('Pushing model and image processor to the hub...'					)
        model.push_to_hub(
            repo_path_or_name=Path(lowerCamelCase_				,					lowerCamelCase_					)				,					organization='nielsr'				,					commit_message='Add model'				,					use_temp_dir=lowerCamelCase_				,					)
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowerCamelCase_				,					lowerCamelCase_					)				,					organization='nielsr'				,					commit_message='Add image processor'				,					use_temp_dir=lowerCamelCase_				,					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path",
     default=None,
     type=str,
     help="Path to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
 )
 parser.add_argument(
     "--model_name",
     default="glpn-kitti",
     type=str,
     help="Name of the model in case you're pushing to the hub.",
 )
 SCREAMING_SNAKE_CASE			: str				   =					parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)










'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()


